基于非完整点云法线滤波补偿的散货船舶舱口识别算法

doi:10.11772/j.issn.1001-9081.2023010051

《计算机应用》唯一官方网站 ›› 2024, Vol. 44 ›› Issue (1): 324-330.DOI: 10.11772/j.issn.1001-9081.2023010051

• 前沿与综合应用 • 上一篇

基于非完整点云法线滤波补偿的散货船舶舱口识别算法

宋郁珉¹, 孙浩², 李湛²^,³(), 李长安⁴, 乔晓澍¹

^1.国能(天津)港务有限责任公司, 天津 300452
^2.哈尔滨工业大学智能控制与系统研究所, 哈尔滨 150001
^3.鹏城实验室数学与理论部, 广东深圳 518055
^4.国家能源集团技术经济研究院, 北京 102211

收稿日期:2023-01-17 修回日期:2023-05-22 接受日期:2023-05-22 发布日期:2023-06-06 出版日期:2024-01-10
通讯作者: 李湛
作者简介:宋郁珉（1981—），男，天津人，高级工程师，硕士，主要研究方向：港口设备智能化控制；
孙浩（2000—），男，山东聊城人，硕士研究生，主要研究方向：点云识别算法、智能控制与优化；
李长安（1982—），男，山东临沂人，正高级工程师，博士，主要研究方向：港口设备智能化控制；
乔晓澍（1988—），男，山东临沂人，工程师，硕士，主要研究方向：港口设备智能化控制。
第一联系人：李湛（1987—），男，河南南阳人，副教授，博士，主要研究方向：智能控制与优化、机器人与智能系统；
基金资助:
国家自然科学基金资助项目(62273122)

Hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation

Yumin SONG¹, Hao SUN², Zhan LI²^,³(), Chang’an LI⁴, Xiaoshu QIAO¹

^1.Guoneng （Tianjin） Port Company Limited，Tianjin 300452，China
^2.Research Institute of Intelligent Control and Systems，Harbin Institute of Technology，Harbin Heilongjiang 150001，China
^3.Department of Mathematics and Theory，Peng Cheng Laboratory，Shenzhen Guangdong 518055，China
^4.CHN Energy Technology & Economics Research Institute，Beijing 102211，China

Received:2023-01-17 Revised:2023-05-22 Accepted:2023-05-22 Online:2023-06-06 Published:2024-01-10
Contact: Zhan LI
About author:SONG Yumin， born in 1981， M. S.， senior engineer. His research interests include intelligent control of port equipment.
SUN Hao， born in 2000， M. S. candidate. His research interests include point cloud recognition algorithm， intelligent control and optimization.
LI Chang’an， born in 1982， Ph. D.， senior engineer. His research interests include intelligent control of port equipment.
QIAO Xiaoshu， born in 1988， M. S.， engineer. His research interests include intelligent control of port equipment.
Supported by:
National Natural Science Foundation of China(62273122)

摘要/Abstract

摘要：

自动装船系统是智能化港口建设的重要组成部分，能够大幅降低港口作业成本，提高经济效益。舱口识别作为自动装船任务的首要环节，成功率和识别精度是后续任务顺利进行的重要保障。由于港口激光雷达的数目和角度等问题，采集所得船舶点云数据时常出现缺失；此外船舶舱口附近经常有大量物料堆积，会使采集到的点云数据无法准确表达舱口的几何信息。由于上述港口实际装船作业中时常出现的问题，显著降低了现有算法的识别成功率，对自动装船作业造成了不良影响，因此迫切需要提升在船舶点云中存在物料干扰或舱口数据缺失的情况下的舱口识别成功率。基于船舶结构特征与自动装船过程中采集的点云数据分析，提出了基于非完整点云法线滤波补偿的散货船舶舱口识别算法。在使用港口实际采集点云所制作的数据集上进行了实验验证，识别成功率和识别精度较Miao和Li的舱口识别算法相比均有提升。实验结果表明，所提算法既能对舱口内物料噪声进行滤除，又能对数据缺失部分进行补偿，能够有效提升舱口识别效果。

关键词: 舱口识别, 非完整点云, 噪声滤除, 数据补偿, 点云轮廓提取

Abstract:

The operating cost of the port can be greatly reduced and economic benefits can be greatly improved by the automatic ship loading system， which is an important part of the smart port construction. Hatch recognition is the primary link in the automatic ship loading task， and its success rate and recognition accuracy are important guarantees for the smooth progress of subsequent tasks. Collected ship point cloud data is often missing due to issues such as the number and angle of the port lidars. In addition， the geometric information of the hatch cannot be expressed accurately by the collected point cloud data because there is often a large amount of material accumulation near the hatch. The recognition success rate of the existing algorithm is significantly reduced due to the frequent problems in the actual ship loading operation of the port mentioned above， which has a negative impact on the automatic ship loading operation. Therefore， it is urgent to improve the success rate of hatch recognition in the case of material interference or incomplete hatch data in the ship point cloud. A hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation was proposed， by analyzing the ship structural features and point cloud data collected during the automatic ship loading process. Experiments were carried out to verify that the recognition success rate and recognition accuracy are improved compared with Miao’s and Li’s hatch recognition algorithms. The experimental results show that the proposed algorithm can not only filter out the material noise in the hatch， but also compensate for the missing data， which can effectively improve the hatch recognition effect.

Key words: hatch recognition, incomplete point cloud, noise filtration, data compensation, point cloud contour extraction

中图分类号:

TP278

宋郁珉, 孙浩, 李湛, 李长安, 乔晓澍. 基于非完整点云法线滤波补偿的散货船舶舱口识别算法[J]. 计算机应用, 2024, 44(1): 324-330.

Yumin SONG, Hao SUN, Zhan LI, Chang’an LI, Xiaoshu QIAO. Hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation[J]. Journal of Computer Applications, 2024, 44(1): 324-330.

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基于非完整点云法线滤波补偿的散货船舶舱口识别算法

Hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation

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